Commerce is entering the era of autonomous execution. While AI tools have thus far focused on enhancing recommendations, personalization, and frontend experiences, the real game-changer will be platforms that empower agents to act, coordinate, and execute commerce flows on behalf of humans across systems, channels, and functions. For enterprise leaders, the question is no longer “Can AI help us?” but “How do we build the architecture, operations, and governance so agents can transact and orchestrate for us at scale?”

According to McKinsey, the upside is enormous. By 2030, the US B2C retail market alone could yield $1 trillion in agent-orchestrated revenue, while globally this figure could reach $3-5 trillion.

This market guide is built for enterprise tech and commerce leaders, CTOs, heads of commerce, and chief digital officers, who are preparing for the next frontier of commerce: agentic orchestration. Over the following sections, you will:

• Understand why commerce is uniquely positioned to become the first major domain of “agent economy” scale
• Get a clear segmentation of the emerging vendor landscape and how to navigate it
• Gain frameworks and criteria to evaluate platforms that move beyond feature-rich storefronts toward backend-driven, agent-led execution
• Discover what differentiates an augmentation-first platform from a true autonomy-capable architecture, and where your organization should sit on that spectrum.

If you’re focused on converting today’s commerce experiments into tomorrow’s fully-scaled, autonomous operations, this guide is your map. The time to architect your agent-ready commerce stack is now.

Why Commerce is the First True Agent Economy

Commerce is structurally primed to become the first major domain where autonomous agents achieve material economic scale. Unlike other enterprise categories where decision boundaries are more ambiguous or contextualized, commerce is defined by high-frequency transactional patterns, rich datasets, clear constraints, and measurable outcomes.

Agents don’t need to “invent” new behaviors, they simply need to execute within well-established commercial models.

Commerce is also inherently machine-compatible. Retail, marketplaces, unified commerce, and supply/demand coordination already operate through standard APIs, catalog data, inventory systems, pricing logic, and fulfillment orchestration. The entire domain has already undergone decades of progressive decomposition into interoperable components, making it not just agent-friendly but agent-ready by design.

This is why the shift toward agentic commerce is not UI-led. It is execution-led. We’ve previously described how building true AI-driven commerce requires moving beyond isolated AI features toward orchestrated, multi-system execution flows, where agents do real operational work, not simply generate suggestions or content. And this also explains the divergence between composable commerce platforms and agentic commerce platforms: composability gives you modularity, but modularity by itself does not automatically yield autonomous execution.

Commerce also has the earliest adoption pressure. Merchandising, inventory balancing, marketplace seller operations, customer care triage, and financial clearing are all domains where marginal decision efficiency translates directly into revenue impact, customer experience, and operational cost control. Agent-executed orchestration across these flows is not conceptual, it has immediate P&L consequences.

And at enterprise scale, this becomes transformative. In this model, operational cycles shift from human-driven batch decisioning toward continuous, real-time response to market signals. Orchestration adjusts dynamically based on live context, system states, and outcome data rather than static rule sets. And AI becomes an embedded runtime capability within the enterprise commerce architecture, not just an auxiliary feature layer added to the experience tier.

This is why commerce will become the first multi-trillion-dollar agent economy—and why it is important to clearly define the platform category now, before the terminology becomes diluted or inconsistently applied across the market.

The Emerging Agentic Commerce Platform Landscape

As more vendors adopt the language of agentic commerce, the underlying capabilities vary dramatically. Some focus primarily on improving discovery and experience layers. Others embed AI into composable platforms to accelerate merchandising or product workflows. And a smaller group is beginning to enable true execution-driven orchestration across systems, domains, and operations.

Our analysis centers on retailer and brand-owned agentic commerce platforms, those operating within an enterprise’s own channels and systems, where data governance, execution authority, and measurable business outcomes remain directly under enterprise control.

To bring clarity to this rapidly evolving landscape, we categorize platforms into four segments based on where agents operate, how much execution authority they hold, and the scope of their impact across the business:

This segmentation forms the basis of the rest of the guide. Each segment represents a valid maturity point, but they do not solve the same class of problem. Treating them as interchangeable, or describing them under a single label, risks obscuring critical architectural distinctions that matter deeply for enterprise AI adoption.

In the next sections, we will examine each category in detail, what defines it, where it is best suited, which vendors exemplify this approach, and where it reaches its limits, so organizations can assess where they stand today and where they need to progress to build an agent-ready commerce future.

1. AI Commerce Assistants

AI Commerce Assistants represent the most widely adopted starting point in the agentic commerce journey. Their core purpose is to enhance the shopping experience: improving search relevance, enriching product discovery, generating contextual content, and guiding shoppers toward faster decisions. While primarily suggestion-led, these assistants can also support transactional actions within the storefront boundary, typically with explicit user confirmation.

Capability Overview: What AI Commerce Assistants Typically Offer

💡 AI Commerce Assistants create high-impact value quickly because they sit closest to the moment of intent, where every marginal gain translates directly into measurable conversion lift.

Example Vendors in this Segment

• Constructor AI Shopping Agent: AI-first search and merchandising platform optimized for behavioral intent signals. Helps retailers create more agentic guidance at the discovery layer by continuously adapting relevance based on shopper behavior.
• Coveo Agentic AI: Agentic AI platform for digital commerce discovery. Extends semantic search and generative reasoning to help shoppers evaluate products more contextually, aligning with early agent-style decision assistance.
• Bloomreach Clarity: AI personal shopping agent for commerce. Guides users through product exploration, asks clarifying questions, and narrows intent — effectively acting as an assistive agent inside the experience tier.

When to Consider AI Commerce Assistants

Best suited for:

• Increasing relevance, findability, and product matching quality quickly
• Achieving measurable conversion uplift without rearchitecting backend systems
• Teams prioritizing rapid experience-layer impact

Limitations to consider:

• Value remains primarily at the UX layer, not operational domains
• Execution impact is incremental, not transformational
• Does not coordinate multi-step workflows or backend system orchestration

2. AI-Augmented Composable Commerce Suites

AI-Augmented Composable Commerce Suites are established commerce platforms now infusing AI agent capabilities within their core modular architecture. Unlike standalone shopping assistants, these platforms embed agents into existing commerce domains such as product catalog, content, cart, checkout, promotions, and returns, using AI to enrich, automate, or partially execute these flows. In this model, “agentic” behavior is introduced as an integrated service layer within the suite’s ecosystem, not as an external application or a fully autonomous runtime.

These capabilities tend to be powerful inside the ecosystem these platforms own, but remain bound to their native domain surface rather than spanning enterprise systems end-to-end.

Capability Overview: What AI-Augmented Commerce Suites Typically Offer

💡 AI-Augmented Commerce Suites introduce agentic behaviors within platform-controlled domains — driving measurable impact inside existing ecosystems, but without extending orchestration beyond them.

Example Vendors in this Segment

• Shopify Magic: Shopify’s AI Assistant Sidekick is built on the Shopify Magic suite and sits within the merchant’s admin to draft, recommend, and execute store-level actions. The retailer’s OpenAI commerce partnership further underscores Shopify’s trajectory toward agentic paths that can participate in shopping flows, while current agentic capabilities remain focused within the Shopify ecosystem.
• Commercetools Agentic Commerce: Commercetools extends its composable commerce platform with agentic execution paths for commerce services. The approach centers on enabling agents to interact with specific modular commerce capabilities through orchestration interfaces and templated prompts within the domain service.
• Shopware AI: Shopware introduces AI as a dual-sided augmentation layer supporting both shopper-facing guidance (discovery, personalization, selection) and merchant-side optimization (catalog tasks, merchandising assistance). This balanced approach strengthens agentic value without requiring organizations to redesign their entire stack.

When to Consider AI-Augmented Commerce Suites

Best suited for:

• Organizations already standardized on these suites, wanting material AI uplift without changing platform strategy
• Merchandising, product ops, experimentation, and conversion optimization across commerce flows
• Teams who prefer agentic extension inside existing stack boundaries rather than open orchestration with external systems

Limitations to consider:

• Agent autonomy is scoped to platform primitives instead of fully orchestrating cross-enterprise decisions or multi-domain workflows
• Extending outside commerce services typically requires custom integration efforts
• Evolution toward fully autonomous enterprise agents may require architectural rethinking later in the maturity path

3. Agent Workflow and Orchestration Engines

Agent Workflow & Orchestration Engines focus on orchestrating commerce-impacting flows across multiple systems, channels, and operational domains — not just within the storefront boundary. These platforms operate across commerce stacks, marketplaces, merchandising ops, fulfillment pipelines, and customer lifecycle surfaces. They introduce agentic capabilities where workflows already span multiple services, vendors, and organizational functions, but autonomy typically still includes human checkpoints and controlled governance steps.

Capability Overview: What Agent Workflow & Orchestration Engines Typically Offer

💡 Agent Workflow & Orchestration Engines expand agentic value beyond the storefront, coordinating cross-system commerce workflows — but their execution remains primarily scoped to commerce domains rather than enterprise-wide orchestration.

Example Vendors in this Segment

• Kibo Commerce: Kibo is known for unified commerce orchestration that spans catalog, personalization, OMS, and fulfillment operations. While not positioned as “agentic commerce” by default, its workflow-centric architecture enables agent-driven coordination across operational surfaces (e.g., inventory exceptions, fulfillment routing, and post-purchase journeys).
• Mirakl Nexus: Nexus is designed to orchestrate marketplace interoperability — connecting brands, sellers, and partner inventory ecosystems. This creates a practical foundation for agentic coordination across multi-party commerce activities such as availability, pricing, approvals, and marketplace lifecycle actions.
• Adobe XP Agent Orchestrator: Agent Orchestrator provides an AI agent orchestration layer across marketing and engagement workflows that directly influence commerce and conversion outcomes. While not commerce-only, it is increasingly used to design agent flows that trigger personalized actions and dynamic interventions across channels that ultimately drive commerce impact.

When to Consider Agent Workflow & Orchestration Engines

Best suited for:

• Marketplace operators and brands managing multi-party commerce models
• Retailers with complex post-purchase operations, fulfillment routing, or returns decisioning
• Organizations wanting orchestration and workflow agents before scaling to full enterprise agent execution platforms

Limitations to consider:

• Agents remain primarily scoped to commerce workflow domains (not full enterprise domains)
• Execution typically still relies on human intervention for the most part
• Scaling into autonomous multi-domain enterprise agents will require an execution-first orchestration core

4. Enterprise Agent Execution Platforms for Commerce

Enterprise Agent Execution Platforms represent the most advanced category of agentic commerce, where agents are not just augmenting workflows, but can be composed, orchestrated, governed, and executed across multiple enterprise domains. These platforms treat agents as modular, composable building blocks that can be mixed, sequenced, stacked, and specialized per business scenario, rather than predefined assistants for fixed use cases.

Critically, these platforms extend beyond commerce surfaces and can influence enterprise-level outcomes: supply chain exceptions, risk evaluation, loyalty tier recovery, finance triggers, partner dispute resolution, and even proactive customer lifecycle intervention (e.g., detecting a high-value customer at risk of churn and triggering a coordinated intervention across support + merchandising).

Capability Overview: What Enterprise Agent Execution Platforms Typically Offer

💡 Enterprise Agent Execution Platforms shift the center of gravity away from “agent features” and toward a composable execution layer — where agents, data, and orchestration become programmable assets that scale across the entire business.

Example Vendors in this Segment

• Rierino: A composable, execution-first agent platform that enables enterprises to orchestrate modular agent workloads across commerce, operations, and adjacent business domains. Its low-code orchestration layer allows teams to design, extend, and govern agent logic efficiently while maintaining full architectural control. Agents can be assembled and managed through event-driven orchestration, enabling proactive and reactive decision automation without locking into predefined agent types.
• Salesforce Agentforce: Salesforce’s new execution-oriented agent framework enabling agents that can operate across CRM, support, service, and revenue workflows. While not commerce-specific, Agentforce represents one of the closest enterprise-scale analogs where agent execution spans multiple functional domains — including commerce-relevant surfaces such as order intervention, service-triggered replenishment, and post-purchase lifecycle optimization.

When to Consider Enterprise Agent Execution Platforms

Best suited for:

• Enterprises that require agents to operate across multiple business functions (not only commerce)
• Organizations with domain-specific orchestration that cannot be solved by single-platform AI assistants
• Companies with high-value operational loops where automation and intervention speed materially affect outcomes (e.g., risk, margin, supply chain, returns, VIP customer care)

Limitations to consider:

• Requires maturity in data readiness, governance models, and cross-functional ownership
• Not ideal for early-stage commerce teams whose needs are purely storefront optimization
• Best ROI when agent scope goes beyond conversion → towards enterprise-wide execution

The Future of Agentic Commerce Platforms

As agentic commerce moves from experimentation to implementation, distinctions between its current segments will gradually blur. AI-powered shopping assistants, agent-augmented suites, workflow engines, and enterprise execution platforms are converging toward a shared destination: composable, execution-intelligent commerce ecosystems. The future will not be defined by who embeds the most AI features, but by who can execute, orchestrate, and govern them across the enterprise.

Emerging concepts such as Agentic Commerce Protocols (ACP), Agentic Commerce Optimization (ACO), and Generative Engine Optimization (GEO) reflect this broader shift. Each points to a more connected and standards-based environment where agents communicate, exchange structured data, and trigger actions across distributed platforms. Yet, momentum will continue to build around retailer- and brand-owned execution layers, where governance, data control, and direct commercial accountability reside.

Commerce leaders evaluating platforms today should think in terms of enterprise readiness rather than AI novelty. The following dimensions can serve as a practical framework for assessment:

• Execution Architecture: Look for event-driven and composable foundations that allow agent behaviors to be orchestrated, versioned, and scaled without hardcoded dependencies.
• Governance & Observability: The ability to monitor, audit, and constrain agent decisions is non-negotiable for enterprise adoption.
• Composability & Extensibility: Agent capabilities should be modular, interoperable, and mixable across business domains, not confined to vendor-defined templates.
• Integration Reach: Evaluate how effectively the platform connects to operational systems, data sources, and external ecosystems beyond its own stack.
• Human-in-the-Loop Design: True maturity lies in balancing automation with human oversight, approvals, and exception handling to maintain accountability.

For most enterprises, the path to agentic commerce will be progressive rather than disruptive — starting with orchestrated workflows and expanding into execution-first automation. The goal is not full autonomy overnight, but to establish the governance, orchestration, and data readiness that make such autonomy safe and measurable.

The next phase of agentic commerce will reward those who treat orchestration as infrastructure, not interface. The real differentiator will not be who deploys the most intelligent agents, but who builds the most intelligent execution layer that scales across systems, teams, and decisions while keeping humans firmly in control of outcomes.

Ready to define your own path in agentic commerce? Get in touch to learn how Rierino supports enterprises in building secure, composable, and execution-ready agentic commerce platforms.